iModMix : Integrative Module Analysis for Multi-omics Data.

Autor:	Narváez-Bandera I; Department of Biostatistics and Bioinformatics., Lui A; Department of Molecular Oncology.; Cancer Biology and Evolution Program, Moffitt Cancer Center., Ayalew Mekonnen Y; Department of Molecular Oncology., Rubio V; Department of Molecular Oncology., Sulman N; Health Informatics Institute, University of South Florida., Wilson C; Department of Biostatistics and Bioinformatics., Ackerman HD; Department of Molecular Oncology.; Cancer Biology and Evolution Program, Moffitt Cancer Center., Ospina OE; Department of Biostatistics and Bioinformatics., Gonzalez-Calderon G; Department of Biostatistics and Bioinformatics., Flores E; Department of Molecular Oncology.; Cancer Biology and Evolution Program, Moffitt Cancer Center., Li Q; Department of Biostatistics, St. Jude Children's Research Hospital., Chen A; Huntsman Cancer Institute, University of Utah., Fridley B; Division of Health Services and Outcome Research, Children's Mercy Research Institute, Kansas City., Stewart P; Huntsman Cancer Institute, University of Utah.; Department of Nutrition and Integrative Physiology, University of Utah.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2024 Dec 17. Date of Electronic Publication: 2024 Dec 17.
DOI:	10.1101/2024.11.12.623208
Abstrakt:	Summary: The integration of metabolomics with other omics ("multi-omics") offers complementary insights into disease biology. However, this integration remains challenging due to the fragmented landscape of current methodologies, which often require programming experience or bioinformatics expertise. Moreover, existing approaches are limited in their ability to accommodate unidentified metabolites, resulting in the exclusion of a significant portion of data from untargeted metabolomics experiments. Here, we introduce iModMix - Integrative Module Analysis for Multi-omics Data , a novel approach that uses a graphical lasso to construct network modules for integration and analysis of multi-omics data. iModMix uses a horizontal integration strategy, allowing metabolomics data to be analyzed alongside proteomics or transcriptomics to explore complex molecular associations within biological systems. Importantly, it can incorporate both identified and unidentified metabolites, addressing a key limitation of existing methodologies. iModMix is available as a user-friendly R Shiny application that requires no programming experience (https://imodmix.moffitt.org), and it includes example data from several publicly available multi-omic studies for exploration. An R package is available for advanced users (https://github.com/biodatalab/iModMix). Availability and Implementation: Shiny application: https://imodmix.moffitt.org. The R package and source code: https://github.com/biodatalab/iModMix.
Databáze:	MEDLINE
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